Machine Learning Approach for Malware Detection Using Random Forest Classifier on Process List Data Structure

2018 ◽  
Author(s):  
Santosh Joshi ◽  
Himanshu Upadhyay ◽  
Leonel Lagos ◽  
Naga Suryamitra Akkipeddi ◽  
Valerie Guerra
Keyword(s):  
Machine Learning ◽  
Data Structure ◽  
Random Forest ◽  
Malware Detection ◽  
Random Forest Classifier ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables

2020 ◽  
Vol 17 (24) ◽  
pp. 9378
Author(s):  
Amy Marie Campbell ◽  
Marie-Fanny Racault ◽  
Stephen Goult ◽  
Angus Laurenson
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Land Surface ◽  
Environmental Changes ◽  
Random Forest Classifier ◽  
Sea Surface Salinity ◽  
Learning Approach ◽  
Climate Variables ◽  
Surface Salinity ◽  
Machine Learning Approach

Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems.

scholarly journals Arabic tweeps dialect prediction based on machine learning approach

2021 ◽  
Vol 11 (2) ◽  
pp. 1627
Author(s):  
Khaled Alrifai ◽  
Ghaida Rebdawi ◽  
Nada Ghneim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Feature Vector ◽  
Random Forest Classifier ◽  
Learning Approach ◽  
Feature Vectors ◽  
Machine Learning Approach ◽  
Important Trait

In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector.

scholarly journals Machine-learning and statistical methods for DDoS attack detection and defense system in software defined networks

2021 ◽  
Author(s):  
Merlin James Rukshan Dennis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Statistical Approach ◽  
Denial Of Service ◽  
Attack Detection ◽  
Learning Approach ◽  
Ddos Attack ◽  
Machine Learning Approach ◽  
Ddos Detection ◽  
Ddos Attack Detection

Distributed Denial of Service (DDoS) attack is a serious threat on today’s Internet. As the traffic across the Internet increases day by day, it is a challenge to distinguish between legitimate and malicious traffic. This thesis proposes two different approaches to build an efficient DDoS attack detection system in the Software Defined Networking environment. SDN is the latest networking approach which implements centralized controller, which is programmable. The central control and the programming capability of the controller are used in this thesis to implement the detection and mitigation mechanisms. In this thesis, two designed approaches, statistical approach and machine-learning approach, are proposed for the DDoS detection. The statistical approach implements entropy computation and flow statistics analysis. It uses the mean and standard deviation of destination entropy, new flow arrival rate, packets per flow and flow duration to compute various thresholds. These thresholds are then used to distinguish normal and attack traffic. The machine learning approach uses Random Forest classifier to detect the DDoS attack. We fine-tune the Random Forest algorithm to make it more accurate in DDoS detection. In particular, we introduce the weighted voting instead of the standard majority voting to improve the accuracy. Our result shows that the proposed machine-learning approach outperforms the statistical approach. Furthermore, it also outperforms other machine-learning approach found in the literature.

scholarly journals Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes

DYNA ◽  
2020 ◽  
Vol 87 (212) ◽  
pp. 63-72
Author(s):  
Jorge Iván Pérez Rave ◽  
Favián González Echavarría ◽  
Juan Carlos Correa Morales
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Approach ◽  
Predictive Capability ◽  
Predictive Capacity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Property Price ◽  
Object Of Study ◽  
Online Pricing

The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assembly (random forest and bagging) show predictive superiority with respect to others. The attributes derived from the text had a significant relationship with the property price (on a log scale). However, their contribution to the predictive capacity was almost nil, since four different attributes achieved highly accurate predictions and remained stable when the sample change.

scholarly journals Metabolomic Approach for Discrimination of Cultivation Age and Ripening Stage in Ginseng Berry Using Gas Chromatography-Mass Spectrometry

Molecules ◽  
2019 ◽  
Vol 24 (21) ◽  
pp. 3837 ◽  
Author(s):  
Seong-Eun Park ◽  
Seung-Ho Seo ◽  
Eun-Ju Kim ◽  
Dae-Hun Park ◽  
Kyung-Mok Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Random Forest ◽  
Gas Chromatography Mass Spectrometry ◽  
Learning Approach ◽  
Ripening Stage ◽  
Ripening Stages ◽  
Machine Learning Approach ◽  
Metabolomic Approach

The purpose of this study was to analyze metabolic differences of ginseng berries according to cultivation age and ripening stage using gas chromatography-mass spectrometry (GC-MS)-based metabolomics method. Ginseng berries were harvested every week during five different ripening stages of three-year-old and four-year-old ginseng. Using identified metabolites, a random forest machine learning approach was applied to obtain predictive models for the classification of cultivation age or ripening stage. Principal component analysis (PCA) score plot showed a clear separation by ripening stage, indicating that continuous metabolic changes occurred until the fifth ripening stage. Three-year-old ginseng berries had higher levels of valine, glutamic acid, and tryptophan, but lower levels of lactic acid and galactose than four-year-old ginseng berries at fully ripened stage. Metabolic pathways affected by different cultivation age were involved in amino acid metabolism pathways. A random forest machine learning approach extracted some important metabolites for predicting cultivation age or ripening stage with low error rate. This study demonstrates that different cultivation ages or ripening stages of ginseng berry can be successfully discriminated using a GC-MS-based metabolomic approach together with random forest analysis.

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